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SUMMARY:Principal components analysis in tree space - Nye\, T (Newcastle)
DTSTART:20110621T153000Z
DTEND:20110621T155000Z
UID:TALK31809@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Phylogenetic analysis commonly gives rise to a collection or s
 ample of inferred evolutionary trees\, each differing from the others. The
 re is a need for methods that visualize\, compare\, and quantify variabili
 ty in such sets of trees\, in terms of both topological and geometrical di
 fferences. Standard tools of multivariate analysis such as multi-dimension
 al scaling and clustering have been applied to sets of trees\, but Princip
 al Components Analysis (PCA) cannot be applied directly since the space of
  evolutionary trees on a fixed set of taxa is not a vector space. I propos
 e a novel geometrical approach to PCA in tree-space that works in an analo
 gous way to standard linear Euclidean PCA. Given a data set of phylogeneti
 c trees\, a geodesic path is sought that maximises the variance of the dat
 a under a form of projection within tree-space onto the path. Geodesic pat
 hs identified in this way reveal and quantify the principal sources of var
 iation in the original collection of trees in terms of both topology and b
 ranch lengths\, and can be visualized as animations of smoothly changing a
 lternative evolutionary trees. The potential of the approach is illustrate
 d by applying tree-space PCA to experimental data from metazoa and a simul
 ation study of long-branch attraction.\n
LOCATION:Seminar Room 1\, Newton Institute
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